School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
Department of Electronic and Electrical Engineering, University of Straclyde, Glasglow G1 1XW, UK.
Sensors (Basel). 2023 Apr 5;23(7):3762. doi: 10.3390/s23073762.
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor-critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
机器人操作面临的挑战，如抓取和物体操作，已经在深度学习强化学习系统的帮助下得到了成功解决。在这篇综述中，我们概述了最近在机器人操作任务中用于深度学习强化学习算法的进展。我们首先概述了强化学习的基本思想和强化学习系统的组成部分。然后介绍了许多针对机器人操作任务提出的深度学习强化学习算法，如基于价值的方法、基于策略的方法和演员-评论家方法。我们还研究了在将这些算法应用于机器人任务时出现的许多问题，以及提出的各种解决方案来处理这些问题。最后，我们强调了几个未解决的研究问题，并讨论了该主题可能的未来方向。
